Diagnosis of Tobacco Related Organ Cancers

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Abstract

This paper titled “Diagnosis of Tobacco-Related Organ Cancers” addresses the critical issue diagnosing Tobacco-Related organ cancers in the human body, by which the majorly affected organs were lung, colon and oral regions, thar are mainly affected due to a high level of tobacco usage. This project uses a Convolutional Neural Network (CNN) algorithm for the accurate detection of these cancers. Various essential metrics were used to evaluate the model’s performance. A web application was also developed to enhance the accessibility and usability, allowing the users to upload medical result images like histopathological images. This application provides a real-time assessment indicating the presence or absence of cancers, thus empowering an individual to make informed decisions. This integrated approach holds great promise for early cancer diagnosis and management, addressing the public health concerns regarding tobacco-related organ cancers.

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Keywords

Tobacco; Convolutional Neural Network (CNN); Histopathological Images (HI); Web Application.